BibTex Citation Data :
@article{Medstat2632, author = {Budi Warsito and Agus Rusgiyono and M. Amirillah}, title = {PEMODELAN GENERAL REGRESSION NEURAL NETWORK UNTUK PREDIKSI TINGKAT PENCEMARAN UDARA KOTA SEMARANG}, journal = {MEDIA STATISTIKA}, volume = {1}, number = {1}, year = {2008}, keywords = {}, abstract = { This paper is discuss about General Regression Neural Network (GRNN) modelling to predict time series data, i.e. the air pollution rate in Semarang City comprises the floating dust, carbon monoxide (CO) and nitrogen monoxide (NO). The GRNN model have four processing layer that are input layer, pattern layer, summation layer and output layer. The input variable is determined by the ARIMA model. The result of GRNN modelling shows that the network have a good performance both at predict in sample and predict out of sample, that can be seen from the mean square error. Keywords: GRNN, predict, air pollution }, issn = {2477-0647}, pages = {43--51} doi = {10.14710/medstat.1.1.43-51}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2632} }
Refworks Citation Data :
This paper is discuss about General Regression Neural Network (GRNN) modelling to predict time series data, i.e. the air pollution rate in Semarang City comprises the floating dust, carbon monoxide (CO) and nitrogen monoxide (NO). The GRNN model have four processing layer that are input layer, pattern layer, summation layer and output layer. The input variable is determined by the ARIMA model. The result of GRNN modelling shows that the network have a good performance both at predict in sample and predict out of sample, that can be seen from the mean square error.
Keywords: GRNN, predict, air pollution
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